Since the advent of electronic information processing, language translation has intrigued developers. Translation between human spoken languages, or so-called “natural” languages (e.g. English, Japanese, German, French, Spanish, etc) has traditionally been performed by a human translation agent conversant in both languages. Automation of the translation operation has been sought after for decades. One reason for difficulty in automating the translation is due to multiple meanings, or definitions for many words. Further, words often do not have an exact corresponding term in another language. Therefore, development of programmed logic for deterministically identifying a translated term in another language has been problematic.
In a natural language, words often have multiple meanings, or definitions. The particular meaning is often determined by other words used in conjunction with a particular word. Therefore, when identifying the meaning of a particular word, a human reader typically considers other words with which it is used. The collection of words in a sequence, such as a sentence or paragraph, defines a context in which the words are used. The context lends an intuitive setting to the words which make particular definitions more likely and other definitions unlikely or nonsensical. The particular intended definition of a word is therefore determined by the context of the words. Stated another way, the meaning of a word may vary with the other words around it. Often, only a particular definition of a word makes sense in a particular context, e.g. eating desert, or traveling across a desert. However, it can be both difficult and tedious to identify a deterministic rule or mechanism to compute a definition of a particular word based on the context implied by the other words with which it is used. Technologies identified by labels such as artificial intelligence and neural nets have purported to simulate the human cognitive recognition of words in context. However, accurate machine recognition of natural languages still fails to attain widespread development and usage. An intangible human component is often employed to identify the proper meaning of a word in a particular context.